1 code implementation • 10 Dec 2024 • Yingying Deng, Xiangyu He, Changwang Mei, Peisong Wang, Fan Tang
Though Rectified Flows (ReFlows) with distillation offers a promising way for fast sampling, its fast inversion transforms images back to structured noise for recovery and following editing remains unsolved.
Ranked #9 on
Text-based Image Editing
on PIE-Bench
no code implementations • 28 Nov 2024 • Yingying Deng, Xiangyu He, Fan Tang, WeiMing Dong
In contrast to existing approaches, we have discovered that latent features in vanilla diffusion models inherently contain natural style and content distributions.
1 code implementation • CVPR 2024 • Chao Liang, Fan Ma, Linchao Zhu, Yingying Deng, Yi Yang
Moreover, we introduce the 3D facial prior to equip our model with control over the human head in a flexible and 3D-consistent manner.
1 code implementation • CVPR 2024 • Yingying Deng, Xiangyu He, Fan Tang, WeiMing Dong
Despite the remarkable progress in image style transfer formulating style in the context of art is inherently subjective and challenging.
1 code implementation • 25 Nov 2023 • Yingying Deng, Xiangyu He, Fan Tang, WeiMing Dong
Despite the remarkable progress in image style transfer, formulating style in the context of art is inherently subjective and challenging.
3 code implementations • CVPR 2022 • Yingying Deng, Fan Tang, WeiMing Dong, Chongyang Ma, Xingjia Pan, Lei Wang, Changsheng Xu
The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content.
Ranked #3 on
Style Transfer
on StyleBench
4 code implementations • 30 May 2021 • Yingying Deng, Fan Tang, WeiMing Dong, Chongyang Ma, Xingjia Pan, Lei Wang, Changsheng Xu
The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content.
no code implementations • 17 Sep 2020 • Yingying Deng, Fan Tang, Wei-Ming Dong, Haibin Huang, Chongyang Ma, Changsheng Xu
Towards this end, we propose Multi-Channel Correction network (MCCNet), which can be trained to fuse the exemplar style features and input content features for efficient style transfer while naturally maintaining the coherence of input videos.
2 code implementations • 27 May 2020 • Yingying Deng, Fan Tang, Wei-Ming Dong, Wen Sun, Feiyue Huang, Changsheng Xu
Arbitrary style transfer is a significant topic with research value and application prospect.
no code implementations • 26 Feb 2020 • Minxuan Lin, Yingying Deng, Fan Tang, Wei-Ming Dong, Changsheng Xu
Controllable painting generation plays a pivotal role in image stylization.